Historically technology has always created new jobs by automating repetitive/mundane work with predictable expected results. Human brains evolve as they learn new skills to solve challenging problems. This leads to creation of new job types which are more evolved yet challenging . e.g. Delivery by drones may take up delivery jobs however this automation itself opens up a number of specialised jobs with higher expertise. E.g. GIS experts, geospatial data analyst/scientist, algorithm designer/ developers, control centre operators, security experts etc.

But hundreds of delivery boys can’t learn these skills – they would lose their jobs unless they evolve. Change is hard especially for the unprepared. But we need to learn from experience. We have come a long way from postcards to emails.

We often come across cases of individuals like a manual tester known to the author. This tester has spent all the 10 years of his career doing manual testing but now he is unable to learn automated testing-as a result he is losing his job. Evolve or perish, that’s the mantra! Some people can’t evolve- either because of inaptitude or inertia.

Not all jobs will have a clear evolution path like typists who evolved by learning word processing as typewriters were being replaced by personal computers. It can be concluded that automation will impact low skilled jobs and create new jobs that use human intelligence and creativity. Upskilling is the way forward for those affected.

Another angle to consider is that we need not work as much as we used to because automation will give us more free time to undertake hobbies or creative and innovative pursuits. But looking at 140 years of data automation has always created more work.

Research says by 2021,four out of ten jobs will be lost to automation. Tasks needing low skills that are highly transactional can be automated using AI & ML. (Artificial Intelligence and Machine Learning)

Like delivery boys losing their jobs to the drones, taxi and truck drivers too will follow suit if Daimler, Tesla & Google succeed in their plans to make vehicles driverless. The list can go on- nursing and health care, insurance underwriters, geographers, nuclear technicians, financial advisors, news reporters, loan officers, accountants and auditors. Many of these jobs will have their next version, different set of OKRs to serve much more evolved customer demand and business models. About 40% of the jobs will go away in the next 5 to 10 years. We can’t say for sure if your job is one of those. Hence its safer to assume that all jobs will become obsolete in the next 10 years.

3) New IT jobs that are recently created by automation

Technology is evolving faster than ever,IBM Watson, Google’s Deep Neural Network (Tensorflow) and several other AI & ML platforms require humans with skills to implement various business models and use cases on those platforms. New breed of systems integrators called Robotic Process Automation (RPA) implementers are building systems with optimised work flow. Skills like DevOps are high in demand due to the need to further automate the service offerings via CI & CD (Continuous Integration and Continuous Delivery). CD results in effective use of infrastructure resources by reducing the “Time To Market” for new products or services in addition to improving the efficiency and reducing the cost. Also in demand is the skill to convert old monolith applications to containerised microservices. Containerised microservices with container orchestration are taking the discipline of DevOps to the next level.

4)Industry verticals most likely to be Impacted by automation

Businesses today are striving for enhancing their customer experience. They are building products and services which will help them to achieve the same results more quickly and effectively. At the same time businesses – both B2C and B2B – are collecting lot of data about behaviour, productivity and performance issues.

E.g. your car goes to refuel itself from the nearest gas station while you sit in your office focusing on work or spending time with family without worrying much about gas level or spending that extra 10 minutes at the gas station. The data required is sent to the service station 24/7. When the car goes to get the servicing done by a fully automated service robot, it already has the data about all the vitals of your car along with exact issues. And this scenario isn’t as far fetched as it sounds and the day isn’t as far as is commonly believed.

Almost all industries are going to leverage the power of AI & ML. To name a few – healthcare, agriculture, manufacturing, transportation, customer service, finance and defence are going to be benefit from these technologies.

As a result new jobs will come into existence. Some of the tasks for almost all the existing jobs will need new skills. All of us have to keep learning new skills. We are all running on a treadmill. Running hard to stay where we are! And the speed is increasing continuously. e.g. some years back DevOps skills were in demand . But today developers are writing containerised microservices. They can both build and deploy without any help from DevOps. This way of working called NoOps is making DevOps skills redundant.

5) Opportunities for IT Professionals in Non-IT Sectors

IT departments in non-IT organizations will benefit from the advances in technology. Presently every company does have a technology department however they will be needing serious updates to the latest technology which will be a challenge in terms of costs to be incurred and new skills to be acquired. Consequently technology would become central to the future strategies of these companies.

To build the smart systems mentioned in point 4 above, we will require skilled IT professionals who are not only capable of developing solutions based on intelligent platforms but are also quick learners. As Non-IT sectors start being heavily driven by IT more techies will be needed to maintain existing systems, to cater to new business cases, to alter existing business cases while ensuring the data security. Governmental rules and regulations will evolve based on various learnings resulting from automation initiatives such as driverless vehicles. This will open up altogether new careers for IT professionals in law enforcement departments.

Right now every company is a software company. Ford sells computers-on-wheels. McKinsey hawks consulting-in-a-box. Fedex boasts a developer skunkworks. IT is employed in every industry today, from manufacturing to agriculture, from logistics to education.

Product Designers, Data Scientists, Digital Marketers, Actuaries, offline auditors and validators, e-tailors, health care professionals, legal advisors. Almost every job in existence today will undergo the next level of evolution. Automation technologies like AI and ML will bring actionable facts or research applicable to a situation or an individual. This information will be leveraged for doing relevant cross-sale / up-sale of products or services. Customer support/ service jobs will be assisted by intelligent chatbots delivering a delightfully engaging and constructively interactive end user experience. In many cases final decisions still have to be made by humans. But these will be very well informed decisions.

These are the non-IT jobs that involve creative skills, emotional and social intelligence, smart networking that will get complemented by automation. Recruitment and selection process too will change with automated parsing, evaluation and shortlisting of resumes based on priorities.

7) How an Individual Should Evolve

There are ample resources available on the internet, free and paid, in the form of video, text, guides which can be leveraged by an individual to re-skill or upskill. No doubts are left about learning opportunities open to anyone when you see a thirteen year old delivering a key note address on IBM Watson and actively contributing to AI & ML. Professionals need to start being more curious about the purpose of what they are doing and how it is impacting the businesses they are working for.Anything that is happening anywhere in the world that is likely to bring about the next level of disruption in their jobs. Use this awarenesss to chart the course of skill acquisition well before the disruption happens. It is not very different when we do financial investment to expect certain amount of returns in future. Investment in oneself has to be a continuous process. People have to work towards building creative skills and being versatile. Those having T-shaped skills would find it easier to learn neighbouring skills. Above all thinking creatively about your future is important. Try making the change instead of coping with it. Finally one has to overcome inertia which is the biggest problem. Unlearning old habits is difficult. Old habits die hard!

8) Jobs or Roles that will Prosper

Roles having more cognitively demanding tasks will be valued more than ever. Here’s an example to put it in perspective. Traditional tellers have almost been replaced by ATMs. The individuals who used to be Tellers are now more focused on customer relationship selling products which are relevant to retail/corporate banking customers. This needs more time to be spent to understand the intricacies of businesses that are served by the banks. The new role is to provide designer solutions to cater to the business needs.

This does increase customer satisfaction and business for the banks. This ultimately results in the increase in number of branches and subsequently in the number of jobs.

This logic of automation creating new jobs applies for many roles which are under threat due to automation (Construction, Healthcare, Services etc..). Improvement in tools and automation, increases the effectiveness of emotional and social skills, creativity and judgments. Consider the example of the automated voice response (AVR) system used for handling lost baggage complaints by airlines. If used without any human intevention; it can put anyone off- it simply lacks the empathy an aggrieved passenger needs.

Having said this, let’s not ignore the fast pace evolution going on in AI and ML and cognitive capabilities been built around them making them more and more self conscious. Synerzip was involved in building an iPad app in which an animated nurse could actually recognize facial expressions. We had to use hundreds of thousands of images of happy, sad, painful and smiling faces to train it. It just goes to show how difficult it is to build an emotionally and socially intelligent bot.

9) Management Help for Employees to Evolve with Automation

If automation is a challenge for employees then it is definitely a challenge for businesses who are employing them. Management has to invest on fostering the culture of multi skill acquisition by their employees. Businesses should proactively introduce the disruptive technologies into work environment and provide training to their employees. Recently Webonise got into the business of implementing Robotic Process Automation(RPA) and IoT solutions. This put demands on the employees to acquire new skills required to develop, implement and maintain these solutions. Thus creating learning opportunities for the employees of Webonise. It was uncertain times for everyone involved. In these times of uncertainty management should develop an atmosphere of fearlessness- do gap analysis and find training needs. Once the training needs are ascertained; they should conduct classroom training followed by hands on reskilling programs. Its an opportunity for the company to develop a learning culture and to motivate every employee to perpetually keep learning and upgrading their skills.

As a part of strategic planning management should tell every employee what his/her next job will be like. Spare no one. There is no one whose job is future-proof. Fearlessness is a pre-requisite for creating a learning atmosphere. Management should measure performance and allow employees to do course correction by providing learning goals and performance objectives. Management should avoid replacing current employees by hiring laterally. Dynamic re-teaming and refitment of jobs would allow cross pollination of ideas and better learning across teams. Finally any good management should set the right employee expectations by making every one aware of the risk of obsolescence and impress the need to continually assess the way automation is impacting our work.

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We often make big upfront commitments , decisions, investments and assume the risk of not achieving the desired outcome.

We hire a software developer or a team of developers at the beginning of the project. Often we are required to define the technology stack and expected level of complexity up front. We wouldn’t be able to hire the team without doing so. Our choice of technology may turn out to be a bad one. Or we might end up committing to a higher budget by hiring a team with higher than the needed level of seniority. But we can’t go back on our commitment and switch to a better team of developers with appropriate level of seniority and expertise in a new technology.

Welcome to the world of smart contracts and blockchains. In not too distant a future it might be possible to enter into a short 2 week contract with the team. (Please read my earlier post) Each developer gets automatically paid in cryptocurrency as their code passes all the automated tests and is deployed in production. At the end of the sprint we have the freedom to suitably modify the team composition by entering into a new set of smart contracts. This gives us the freedom to experiment and evaluation a few options.

What changed is our ability to enter into smaller , more frequent transactions without the need of a central aggregator such as a Bank or the Corporate HR department. Aggregators can’t economically work with shorter contracts and smaller payments because there is a fixed overhead associated with each contract and payment.

The table below shows a few more examples where such a change can occur.

Big Upfront

Lean Experimental

Buying a car

Sharing a ride- No aggregator such as Uber or credit card needed.

Buying Servers

Virtual servers on the cloud can be rented on pay as you use basis- going forward there will be no need to use credit cards. Cloud provider will automatically receive their payment in cryptocurrency.

Buying heavy machinery

Renting made possible by more details about usage, wear and tear and damage if any will get communicated by data streamed by IOT sensors

Launching a company to enter a new business with all the associated overheads

Assembling a team loosely bound by smart contracts. Blockchain ensures compliance without the protection of corporate law

Group health insurance- healthy members pay more and those susceptible to health issues pay less. Claiming insurance is fraught with legalese embedded in fine print.

Wearable devices communicate the current health status frequently enough that makes it possible for a healthcare provider to directly enter into a smart contract with the individual .

This new way of working has become even more possible because of recent advances in IOT which will enable digitalization of lot of information about the physical world. Broader acceptance of “shared economy” is pushing individuals to “rent & try” instead of “buy & regret”. All this works very well for those who think and work in the lean and agile way. It gives them the freedom to conduct experiments to validate their hypotheses before making a large commitment. It lowers the barrier to entry into businesses which were a territory exclusively reserved for the big corporations.

Scope of this article is limited to innovative /learning organizations that are responding to constantly changing world. Traditional hierarchical organizations like the military or church are out of scope.

Distributed Autonomous Organizations (DAOs)will come into existence. These will have the advantages communities have over hierarchical structures. Individual team members will be bound by smart contracts and there won’t be any need for a corporate entity to govern their relationships.

The risk of failure of a project will be assumed by an individual stakeholder who will be rewarded as the owner of the outcome if it succeeds.

These DAOs will be short lived – their lives will be limited to the project for the purpose of which the team is put together. Once the project is over the team will be disbanded.

Team members will be bound by a smart contract to the DAO. These smart contracts will ensure that no central authority is required to enforce the terms of the contract.

There will be no managerial overhead required to supervise individual team members and to ensure that they are delivering the work that they are being paid for. Smart contracts will automatically enforce accountability and discipline.

Work itself will be more interesting with one individual playing multiple roles – a leader in one project could be an intern in another project.

With smaller teams engaged in number of small short lived DAOs executing innovative projects the job of hiring team members will become more frequent. One can’t spend 12 weeks (as in the traditional recruitment process )searching for and evaluating candidates for a project which might last for 20 weeks.

More automated ways of recruitment using AI and Machine Learning will help recruiters and DAOs to reduce the time to hire to a few days – not weeks. Teams will get formed by like minded individuals finding each other. Trust relationships will be more easily forged by smart contracts.

Jobs will be replaced by assignments. An individual would be engaged in multiple assignments simultaneously. Each assignment will be customized to suit the needs of the project. Working hours, place of work, compensation, skills needed etc. will depend on the project. Designations will be replaced by multiple roles

Individuals will develop T shaped skills with depth in one area and interest and working knowledge of many other areas. In fact they will relish learning new skills by offering to work under a relatively younger team member. Reporting relationships will become more flexible- one can be reporting to a person who is a reportee on another project.

DAOs will pay individual team members in cryptocurrencies which will ensure that the economy in which the DAO is participates will thrive increasing the value of the cryptocurrency as compared to the fiat currency.

Monthly earning will depend on the value added and will reduce over the years if new skills are not acquired. There will be less politics as people will stop holding higher authority and getting paid higher merely because of their seniority. Authority and leadership will be short lived and based only on merit.

There will be a wider acceptance of the fact that intellectual property grows by sharing it openly , by maximizing its use and inclusion than by restricting its use and exclusion. Open source culture will gain ground. IP rights, patents and litigations will fade and become history.

Smart contracts will ensure that royalty, license fees and commissions will be automatically paid directly to the creator triggered by use of the digital asset. Intermediaries aggregating , distributing and collecting payments for digital assets will go out of business – the way music publishers like HMV and Nickelodeon went. Creative talent will be richly rewarded leading to an age of ultra modern renaissance.

Individuals will stop taking comfort in “getting hired” as the project to which they are assigned may not last more than a few weeks. Lifetime / Long Term employment with retention bonus etc. will become things of the past.

Smart contracts won’t need corporate law to ensure implementation. Cryptocurrencies would isolate these contracts from vagaries of fiat currency fluctuations. People working across national borders will be able to enter into contracts and get paid for their work without bothering about corporate laws of any particular nation. Geographical proximity would become less relevant. Teams will be formed based on mental proximity than physical proximity.

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Every thought leader after Deming has extolled the virtue of measuring whatever we need to improve. I recently read this article – it suggests seven metrics to measure a recruiter’s performance. Many more articles and suggestions for building performance scorecards are published. We need some simple metrics that could be quickly ascertained without investing in specialized software etc. In this article we are exploring two such metrics to measure software recruiters’ performance and ways of improving the same. By definition a software recruiter specializes in hiring software professionals.

In this post we will focus on the recruitment process including recruiters, hiring managers, other members of the interview panels, recruitment consultants and agencies and candidates. Combined effect of their individual behaviors results in the inefficiencies of the recruitment process.

Here are some typical characteristics of the software job-seekers’ market. Candidates often claim a lot more in their resumes than their real “hands-on experience”. Recruiters – particularly those who are experts at Boolean search rely a lot on what is claimed in the resume and base their search on keywords and extrapolate an individual’s capabilities based on the companies worked for and the schools attended. The best way to separate substance from hype is by having a short telephonic conversation. Just a few questions would have the candidate himself telling where his or her real strengths are and what should be ignored.

At this stage let us introduce two metrics to measure the efficiency of a source such as a recruiter or an agency providing candidates.

Recall of a source measures its reliability or spread of coverage of the the total population of suitable candidates. This is tough to measure as we don’t know the “total population” of suitable candidates who are currently looking for a change. As a proxy we can replace the “total population” with “total known number” by adding number of suitable candidates sourced from all sources including employee referrals, direct applicants, agencies and recruiters.

Precision of a source measures how many suitable candidates were sourced as a percentage of the total number of candidates sourced. This shows what percentage of the sourced candidates were useful and what percentage of the sourcing effort resulted in “waste”. This is measured easily by taking a ratio of candidates who are found worthy of second interview over total number of resumes coming from the source.

Candidates sourced but not found suitable are called false positives – our effort on interviewing these candidates is wasted and needs to be minimized. Similarly candidates who were suitable but were not sourced are called false negatives– indicating lower reliability of the source in terms of its ability to find suitable resources.

The main reason for false positives is due to the fact that many recruiters and agencies are singularly focused on improving recall. Their intent is to improve the probability of finding a match by sourcing as many resumes as possible. This “spray and pray “approach results in a lot of wasted effort in interviewing false positives.

On the contrary if a recruiter applies a filter and reduces the total number sourced by having a preliminary telephonic round , it will reduce false negatives and improve precision. An upside of this approach results in a better deal for the hiring managers who have less interviewing but better results.

Majority of hiring managers believe that recruiters can’t really do any technical screening. Recruiters do “keyword” based search – not really going deeper to find out if the candidate really has the relevant technical skills. This results in a communication gap between the recruiters and the hiring managers. Hiring managers don’t think that feedback any more detailed than “Technically Unsuitable” would be understood by the recruiters.

We believe that recruiters can be trained to do preliminary technical screening. Some amount of guidance in the form of technical questions that weed out obviously unsuitable candidates can improve the recruiters’ ability to judge.

If we have more meaningful feedback coming more frequently; it will improve the precision and reduce wasted effort and interviewing fatigue. Smaller batch sizes would help get early feedback resulting in corrective action of improved technical filtering. Baby steps of small batches each one improving precision in an iterative way seems like the way we should hire technical talent.

Main Takeaway- Continuous Experimentation Well Beyond The Startup Stage

Contrary to the generally held belief that lean startup principles advice experiments in early stages of a startup; many speakers at the conference showed how they are experimenting continuously at all stages of their ventures.

Eric Ries in said that “Product market fit and experimentation is not a one time activity. It’s a continuous flow of activities. There are no discrete big jumps! Think of these steps in continuous flow that lend themselves to go back if an experiment fails”

Hiten Shah of Kissmetrics reiterated that a meaningful metric leads to a hypothesis and then to an experiment to validate it. Startups should always be A/B testing. Empirically 1 out of 5 tests succeed. Strive to win 1.67 out of 5.

A/B testing can help not only at different stages of a startup; but also for various activities including website traffic, app installs, welcome emails, Web/Mobile onboarding, E-mail digests, Triggered notifications, dormant/churned users.

Des Traynor also said that having continuous feedback is more valuable than one time event driven feedback.

Experiments helped even established brands like Rally, Google and Vox Media to validate hypotheses at later stages of their product lifecycle

Rally launched a dummy brand waffle.io targeted towards developers to protect the parent brand from the impressions created by the experiments. Finally Rally decided to have both brands.

Google Adsenses team validated Partner Problems using Lean Startup Principles. Blair Beverly said that they faced problem with new projects scaling too early and failing as they lacked historical data to go by. He got coworkers at Google ad senses team to use the Lean Startup. They scheduled office time to read the book, being helpful and not pushy. They also gave them a reading guide with questions. In the end they identified three hypotheses; put together templates like the partner problem hypothesis. People felt good about invalidating their own hypotheses as it saved them work that would’ve been wasted.

Vox Media launched Vox.com in 9 weeks using analytics to guide customer validation. Melissa Bell got her co founders and others from Vox media in the same room to get everyone on the same page about her vision. Many editorial staff came from Washington Post whereas Vox was an agile technology company. They used card stacks for flexibility. They had problems with the way editors used card stacks, as it was difficult to navigate-hence they analytics were used to solve the problem. Now Vox.com has 22m users. Delivering content to users where they are-on social channels such as Facebook or YouTube instead of own URL.

You need to eat your own dog food. Use your product to solve your own problems. If not you are at an enormous disadvantage.

Invert the org chart :customers & customer facing team should be on top. They should be heard and not told what to do.

Force yourself to pretend at the earliest possible moment what you want to be- to learn whether its worth being what you want to be. Landing pages, Concierge or Wizard of Oz are ways to pretend.

Don’t speed up for the sake of it. For startups not going fast enough is not the main risk. False summit is the reality. Journey of a startup is slow like that of a mountaineer. A new goal appears once you have reached what seemed like the ultimate goal.

According to Grace Ng success criterion for any experiment is the weakest outcome that will give you enough confidence to move forward.

Testing the riskiest assumption on buy side in a two-sided market place could be deceptive in a sellers’ market. Sellers may not automatically follow even if you find many buyers.

Validated hypothesis doesn’t necessarily lead to a viable business. Grace Ng tested a hypothesis whether birdwatchers will post photos to ask questions. The Hypothesis was valid but the problem turned out to be too small – not a big pain-point.

Don’t validate the solution before validating the problem. As in the case above; the problem was not big enough though the solution was right.

As you go through build-measure-learn cycles for product the same way you should be going thru build-measure-learn cycles for process compliance.

Beware of developers’ tendency to focus on how to do things than on outcomes. Developers tend to ignore security as they are dazzled by technology, so they focus on doing things faster-not safer. Security testing, threat model and risk metrics should be included right from the beginning and not at the end.

Don’t as two questions that kill breakthrough innovation – what is the roi? When do we get it? In order to answer these questions we have to look at existing markets which kills innovation -innovator’s dilemma. We need to build cultures or safety net for innovators.

Conclusion

Most of the takeaways and dos and don’ts are common sense for any practicing entrepreneur. According to Eric Ries The Lean Startup process is more widely practiced than talked about. Most entrepreneurs are agents of long term change. They don’t think The Lean Startup is a big deal. As with most profound thoughts- it seems obvious after its well thought through, well organized and well presented.

Starfish and Spider
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The Black Swan
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The Four Steps To The Epiphany
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